2026-27 Project (Zimmerman & Hargreaves & Seedat)

Injury Surveillance Information Systems and Health Inequalities: Advancing Risk Detection for Workers in Low-Wage, High-Risk Occupational Groups

SUPERVISORY TEAM

Supervisor

Professor Cathy Zimmerman at LSHTM
Faculty of Public Health & Policy, Department of Global Health and Development
Email: cathy.zimmerman@lshtm.ac.uk

Co-Supervisor

Professor Sally Hargreaves at City St George’s
School of Health & Medical Sciences, Department of Global, Public and Population Health and Policy
Email: s.hargreaves@sgul.ac.uk

Co-Supervisor

Dr Farah Seedat at City St George’s
School of Health & Medical Sciences, Department of Medicine
Email: fseedat@sgul.ac.uk

PROJECT SUMMARY

Project Summary

This PhD project offers an opportunity to work with cross-country partners generate global evidence to strengthen Injury Surveillance Information Systems designed to protect migrant and young workers, especially those in low-wage and precarious occupations. The successful student will develop a multi-disciplinary skill set, integrating social epidemiology, participatory methods from behavioural science, and advanced statistical and data science techniques. The project includes systematic reviews, qualitative, participatory fieldwork with peer researchers, and advanced analysis of international datasets, focusing on topics such as climate-linked risks, gig work, and labour exploitation. The position involves cross-country work and collaboration with leading global partners, including the International Labour Organization (ILO), International Organization for Migration (IOM), WHO and universities in Indonesia, Malaysia, Egypt, and the UK.

Project Key Words

Migration, occupational health, Injury Surveillance Systems

MRC LID Themes

  1. Global Health
  2. Translational and Implementation Research
  3. Health Data Science

Skills

MRC Core Skills

  1. Interdisciplinary skills
  2. Quantitative skills

Skills we expect a student to develop/acquire whilst pursuing this project:

  • Advanced statistical analysis techniques and basic modelling to assess and improve ISIS collection, analysis and reporting.
  • Systematic review and meta-analysis – including use of meta packages, Rayyan and PRISMA/PROSPERO.
  • Use of R and other statistical tools and packages for advanced statistical analysis of large datasets.
  • Participatory, co-production techniques with peer-researchers, and qualitative analysis using NVivo.

Routes

Which route/s are available with this project?

  • 1+4 = Yes
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • City St Georges – Master of Public Health MPH
  • City St Georges – MSc Global Health
  • LSHTM – MSc Demography & Health
  • LSHTM – MSc Epidemiology
  • LSHTM – MSc Health Data Science

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes

Location & Travel

Students funded through MRC LID are expected to work on site at their primary institution. At a minimum, all students must meet the institutional research degree regulations and expectations about onsite working and under this scheme they may be expected to work onsite (in-person) more frequently. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training for research degree study and training. Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Day-to-day work (primary location) for the duration of this research degree project will be at: LSHTM – Bloomsbury, London

Travel requirements for this project: Possible site visits include:  Kuala Lumpur  Cairo, Egypt

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • Minimum standard institutional eligibility criteria for doctoral study at LSHTM
  • Preference for statistics, demography, epidemiology but not essential if taking up MSc option.

Other useful information

  • Potential Industrial CASE (iCASE) conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

Estimates from 2019 show there were 2.9 million deaths attributable to work – 2.58 million from work-related diseases and 320,000 from occupational injuries (Takala, 2024). Over 92% of work-related deaths among people aged 15-69 occurred in low- and middle-income countries (LMICs) (Wu, 2018). Migrant workers in low-skilled and hazardous jobs are exposed to significantly higher rates of injury and death than native-born workers (Lau, 2024). Workers aged 15 to 24 are up to 2.3 times more likely to get injured on the job than workers over 25 according to US data. Injury Surveillance Information Systems (ISIS), particularly in LMICs, remain inadequate for policy-making and Global Burden of Disease measures due to poor inclusion of marginalized and informal workers, poor standardization, and weak data integration (Mirani, 2023).

PROJECT OBJECTIVES:
This project seeks to improve the inclusion of marginalized workers and occupational risks in ISIS and promote inclusive measures of the global burden of disease by:

  1. Identifying both current and emerging occupational risks among workers in low-wage sectors;
  2. Documenting experiences and views of migrant and young workers regarding occupational exposures, health outcomes, and access to care;
  3. Synthesizing data on migrant and young workers to profile occupational health threats and outcomes such as heat stress, gig work, exploitation, trafficking, violence, and social determinants including migration status and young age (14-24 years); 
  4. Evaluating current ISIS use, especially in LMICs, and making recommendations for improved inclusion of marginalized groups and new occupational threats.

TECHNIQUES TO BE USED:

  • Systematic review and meta-analysis: Following PRISMA guidelines, review both published and grey literature to assess prevalence, types, and outcomes of occupational exposures-including climate-related and precarious/gig work-among migrants and young workers globally.
  • Qualitative participatory research: Co-produce and conduct focus group discussions with migrant and young gig workers to understand perceptions of workplace risks, experiences with injury/illness, and barriers to seeking care.
  • Statistical analysis of large datasets: Analyze occupational morbidity and mortality for migrant and young workers using data from sources such as NEISS, NEISS-AIP, WISQARS, WHO Minimum Data Set, trauma registries, and ILO labour force, child labour, and forced labour surveys.
  • Advanced risk modeling: Evaluate how well current ISIS datasets capture migrant status, age, work type, and climate/gig exposures. Use statistical and machine learning models to identify risk factors, predict outcomes, and develop recommendations for more accurate and inclusive data collection.   

CONFIRMED AVAILABILITY OF ANY REQUIRED DATABASES OR SPECIALIST MATERIALS:

  • DATABASES. The project will draw on publicly available datasets such as NEISS, NEISS-AIP, WISQARS, WHO Minimum Data Set for Injury Surveillance, and, where available, trauma registries. The student will also be able to collaborate with the International Labor Organisation, utilizing their Labour Force, Forced Labour, and Child Labour surveys from multiple countries.
  • SPECIALIST MATERIALS. The project will have access to workers for participatory and co-produced components via current research partners in the UK, Malaysia, Egypt, and Indonesia.

RISK MITIGATION:
The disparate quality and completeness of global datasets may pose challenges, but by involving international experts, leveraging diverse data sources, and conducting a comprehensive assessment, the project will generate robust evidence to support policy action and improve global occupational health surveillance.

Further reading

Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)

Other pre-application materials:

Additional information from the supervisory team

The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.

Zimmerman & Hargreaves & Seedat Recording

MRC LID LINKS

To apply for a studentship: MRC LID How to Apply
Full list of available projects: MRC LID Projects
For more information about the DTP: MRC LID About Us

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